101 lines
2.7 KiB
Matlab
101 lines
2.7 KiB
Matlab
function CovarianceMatrix = nancovariance(data)
|
|
% Computes the covariance matrix of a sample (possibly with missing observations).
|
|
|
|
%@info:
|
|
%! @deftypefn {Function File} {@var{CovarianceMatrix} =} compute_corr(@var{data})
|
|
%! @anchor{compute_cova}
|
|
%! This function computes covariance matrix of a sample defined by a dseries object (possibly with missing observations).
|
|
%!
|
|
%! @strong{Inputs}
|
|
%! @table @var
|
|
%! @item data
|
|
%! a T*N array of real numbers.
|
|
%! @end table
|
|
%!
|
|
%! @strong{Outputs}
|
|
%! @table @var
|
|
%! @item CovarianceMatrix
|
|
%! Array of real numbers.
|
|
%! @end table
|
|
%!
|
|
%! @strong{This function is called by:}
|
|
%! @ref{descriptive_statistics}.
|
|
%!
|
|
%! @strong{This function calls:}
|
|
%! @ref{ndim}, @ref{demean}, @ref{nandemean}.
|
|
%!
|
|
%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.cova} is a
|
|
%! @tex{n\times n} vector (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}).
|
|
%!
|
|
%! @end deftypefn
|
|
%@eod:
|
|
|
|
% Copyright © 2011-2014 Dynare Team
|
|
%
|
|
% This file is part of Dynare.
|
|
%
|
|
% Dynare is free software: you can redistribute it and/or modify
|
|
% it under the terms of the GNU General Public License as published by
|
|
% the Free Software Foundation, either version 3 of the License, or
|
|
% (at your option) any later version.
|
|
%
|
|
% Dynare is distributed in the hope that it will be useful,
|
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
% GNU General Public License for more details.
|
|
%
|
|
% You should have received a copy of the GNU General Public License
|
|
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
|
|
|
% Initialize the output.
|
|
CovarianceMatrix = zeros(size(data,2));
|
|
|
|
if isanynan(data)
|
|
data = bsxfun(@minus,data,nanmean(data,1));
|
|
for i=1:size(data,2)
|
|
for j=i:size(data,2)
|
|
CovarianceMatrix(i,j) = nanmean(data(:,i).*data(:,j));
|
|
if j>i
|
|
CovarianceMatrix(j,i) = CovarianceMatrix(i,j);
|
|
end
|
|
end
|
|
end
|
|
else
|
|
data = bsxfun(@minus,data,mean(data,1));
|
|
CovarianceMatrix = (transpose(data)*data)/size(data,1);
|
|
end
|
|
|
|
%@test:1
|
|
%$
|
|
%$ % Define a dataset.
|
|
%$ data1 = randn(10000000,2);
|
|
%$
|
|
%$ % Same dataset with missing observations.
|
|
%$ data2 = data1;
|
|
%$ data2(45,1) = NaN;
|
|
%$ data2(57,2) = NaN;
|
|
%$ data2(367,:) = NaN(1,2);
|
|
%$
|
|
%$ t = zeros(2,1);
|
|
%$
|
|
%$ % Call the tested routine.
|
|
%$ try
|
|
%$ c1 = nancovariance(data1);
|
|
%$ t(1) = 1;
|
|
%$ catch
|
|
%$ t(1) = 0;
|
|
%$ end
|
|
%$ try
|
|
%$ c2 = nancovariance(data2);
|
|
%$ t(2) = 1;
|
|
%$ catch
|
|
%$ t(2) = 0;
|
|
%$ end
|
|
%$
|
|
%$ if t(1) && t(2)
|
|
%$ t(3) = max(max(abs(c1-c2)))<1e-4;
|
|
%$ end
|
|
%$
|
|
%$ % Check the results.
|
|
%$ T = all(t);
|
|
%@eof:1 |